AI-Driven Visual Inspection for Predicting Meat Freshness and Quality
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
|
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866902023003176960 |
|---|---|
| author | Raj, Abhishek Vijay, B K Sai Kundu, Sourin Arasi, Ehzil |
| author_facet | Raj, Abhishek Vijay, B K Sai Kundu, Sourin Arasi, Ehzil |
| contents | <p>ABSTRACT To make meat fresh and safe is a real issue for the food industry. A computer vision-based system with artificial intelligence methods to forecast the quality of one of the widely eaten meats in India: chicken. The suggested system takes photos of meat samples and converts them from the RGB to HSV color space in order to more effectively isolate hue, saturation, and brightness features that are indicative of spoilage and freshness. In contrast to conventional binary classification systems, this model scores meat on a scale of four classes—fresh, edible, spoiled, and toxic—to provide a more nuanced evaluation. Furthermore, the system utilizes regression models to forecast the shelf life of remaining meat and suggests proper storage conditions (e.g., refrigerate or freeze) to maintain quality. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17489799 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Driven Visual Inspection for Predicting Meat Freshness and Quality Raj, Abhishek Vijay, B K Sai Kundu, Sourin Arasi, Ehzil <p>ABSTRACT To make meat fresh and safe is a real issue for the food industry. A computer vision-based system with artificial intelligence methods to forecast the quality of one of the widely eaten meats in India: chicken. The suggested system takes photos of meat samples and converts them from the RGB to HSV color space in order to more effectively isolate hue, saturation, and brightness features that are indicative of spoilage and freshness. In contrast to conventional binary classification systems, this model scores meat on a scale of four classes—fresh, edible, spoiled, and toxic—to provide a more nuanced evaluation. Furthermore, the system utilizes regression models to forecast the shelf life of remaining meat and suggests proper storage conditions (e.g., refrigerate or freeze) to maintain quality. </p> |
| title | AI-Driven Visual Inspection for Predicting Meat Freshness and Quality |
| url | https://doi.org/10.5281/zenodo.17489799 |